Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f9fdd9686d8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f9fdd8e7748>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = "real_input")
    z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, name = 'learning_rate')
    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
#global hyperparams for dropout
keep_prob_generator = 0.5
keep_prob_discriminator = 0.5
In [15]:
#global leaky relu hyperparam
leaky_relu_value = 0.2

#global leaky relu helper function
def lrelu(x, name="lrelu"):
    return tf.maximum(x, leaky_relu_value * x, name=name)
In [14]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    with tf.variable_scope("discriminator",reuse=reuse):
        #alpha=0.2

        l1 = tf.layers.conv2d(images, 
                              64, 
                              5, 
                              strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        l1 = tf.layers.dropout(l1, rate=0.5, training=True)
        l1 = lrelu(l1)

        l2 = tf.layers.conv2d(l1,
                              128, 
                              5, 
                              strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))

        l2 = tf.layers.dropout(l2, rate=0.5, training=True)
        l2 = tf.layers.batch_normalization(l2,training=True)
        l2 = lrelu(l2)
        
        l3 = tf.layers.conv2d(l2, 
                              256, 
                              5, 
                              strides=2, 
                              padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))

        l3 = tf.layers.dropout(l3, rate=keep_prob_discriminator, training=True)
        l3 = tf.layers.batch_normalization(l2,training=True)
        l3 = lrelu(l3)
        
        flat = tf.reshape(l2, (-1,7*7*256))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [16]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """

    reuse = not is_train
    
    with tf.variable_scope("generator", reuse=reuse):
        
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = lrelu(x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 
                                        256, 
                                        4, 
                                        strides=1, 
                                        padding='same',
                                        kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        x2 = tf.layers.dropout(x2, keep_prob_generator, training=is_train)
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = lrelu(x2)

        # 7x7x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 
                                        128, 
                                        4, 
                                        strides=2, 
                                        padding='same',
                                        kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        x3 = tf.layers.dropout(x3, keep_prob_generator, training=is_train)
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = lrelu(x3)

        # 14x14x128 now

        # 28x28x(out_channel_dimension) now
        logits = tf.layers.conv2d_transpose(x3, 
                                            out_channel_dim, 
                                            5, 
                                            strides=2, 
                                            padding='same')
        return tf.tanh(logits)



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [17]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    # get counterfit from generator, 
    # get output from discriminator using real input
    # get output from discriminator using counterfit generated
    g_out = generator(input_z,out_channel_dim=out_channel_dim)
    d_real_out, d_real_logit = discriminator(input_real)
    d_fake_out, d_fake_logit = discriminator(g_out,reuse=True)
    
    #real label smoothing
    smooth = 0.1
    
    # shortcut loss calculation function
    calculate_loss = lambda logits,labels : tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,labels=labels))
    
    real_label = tf.ones_like(d_real_out) * (1 - smooth)
    fake_label = tf.zeros_like(d_fake_out)
    
    d_loss_real = calculate_loss(d_real_logit, real_label)
    d_loss_fake = calculate_loss(d_fake_logit, fake_label)
    
    g_loss = calculate_loss(d_fake_logit, tf.ones_like(d_fake_out))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [18]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    
    # get variables for `var_list` minimize parameter
    trainables = tf.trainable_variables()

    # filter those by generator/discriminator
    gen_train = [operation for operation in trainables if operation.name.startswith('generator')]
    dis_train = [operation for operation in trainables if operation.name.startswith('discriminator')]

    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    # use with control dependencies for batch normalization calculations updating
    with tf.control_dependencies(g_update_ops):
        # calculate optimizations
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list=gen_train)

    # use with control dependencies for batch normalization calculations updating
    with tf.control_dependencies(d_update_ops):
        # calculate optimizations
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list=dis_train)
        
    return g_train_opt, d_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [20]:
import datetime


def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    
    real_input, z_input, lr = model_inputs(data_shape[1],
                                           data_shape[2], 
                                           data_shape[3], 
                                           z_dim)
    
    d_loss, g_loss = model_loss(real_input, 
                                z_input, 
                                data_shape[3])
    
    d_opt, g_opt = model_opt(d_loss,
                             g_loss,
                             learning_rate,
                             beta1)
    
    steps, step_text_update, step_example_update = 0, 10, 100
    sample_size = 25
    display_count, display_max_count = 0,31
    
    #calculates how many images outputs will occur
    max_multiplier = 64/batch_size
    display_max_count *= max_multiplier
    
    #debug calculate start time
    start = datetime.datetime.now()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps+=1
                
                # resize b/c we used -.5 to .5 instead of -1 to 1
                batch_images = batch_images * 2
                
                # random sample noise 
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                #run optimizers
                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: batch_z})
                _ = sess.run(g_opt, feed_dict={z_input: batch_z, lr: learning_rate, real_input: batch_images})
#                 #run twice to speed up generator training
#                 _ = sess.run(g_opt, feed_dict={z_input: batch_z, lr: learning_rate, real_input: batch_images})
                
                if steps % step_text_update == 0:
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("epoch {}/{} ***".format(epoch_i+1, epochs),
                          "discriminator loss: {:.4f} ***".format(train_loss_d),
                          "generator loss: {:.4f}".format(train_loss_g))                    
                
                if steps % step_example_update == 0:
                    display_count += 1
                    print("progress: {0:.0f}%".format(display_count/display_max_count * 100))
                    show_generator_output(sess, sample_size, z_input, data_shape[3], data_image_mode)
                    
    #Debug calculate total time taken
    end = datetime.datetime.now()
    dif = end - start
    time_taken = divmod(dif.days * 86400 + dif.seconds, 60)
    print(time_taken)
    print("COMPLETE")

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [22]:
batch_size = 32
z_dim = 128
learning_rate = 0.0009
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
epoch 1/2 *** discriminator loss: 1.4671 *** generator loss: 1.6160
epoch 1/2 *** discriminator loss: 1.5746 *** generator loss: 0.8975
epoch 1/2 *** discriminator loss: 1.0690 *** generator loss: 0.9231
epoch 1/2 *** discriminator loss: 0.9709 *** generator loss: 1.3726
epoch 1/2 *** discriminator loss: 1.5397 *** generator loss: 2.4354
epoch 1/2 *** discriminator loss: 1.4311 *** generator loss: 1.7084
epoch 1/2 *** discriminator loss: 1.1945 *** generator loss: 1.6719
epoch 1/2 *** discriminator loss: 1.2177 *** generator loss: 1.4287
epoch 1/2 *** discriminator loss: 1.4236 *** generator loss: 1.0778
epoch 1/2 *** discriminator loss: 1.5580 *** generator loss: 0.3998
progress: 2%
epoch 1/2 *** discriminator loss: 1.4662 *** generator loss: 0.9267
epoch 1/2 *** discriminator loss: 1.3486 *** generator loss: 1.0905
epoch 1/2 *** discriminator loss: 1.6295 *** generator loss: 1.7449
epoch 1/2 *** discriminator loss: 1.3899 *** generator loss: 1.0489
epoch 1/2 *** discriminator loss: 1.3642 *** generator loss: 1.3256
epoch 1/2 *** discriminator loss: 1.4825 *** generator loss: 0.8777
epoch 1/2 *** discriminator loss: 1.5938 *** generator loss: 1.5201
epoch 1/2 *** discriminator loss: 1.2381 *** generator loss: 0.9725
epoch 1/2 *** discriminator loss: 1.2622 *** generator loss: 0.6107
epoch 1/2 *** discriminator loss: 1.0854 *** generator loss: 1.1745
progress: 3%
epoch 1/2 *** discriminator loss: 1.9418 *** generator loss: 2.5783
epoch 1/2 *** discriminator loss: 1.4193 *** generator loss: 0.8067
epoch 1/2 *** discriminator loss: 1.4570 *** generator loss: 2.0044
epoch 1/2 *** discriminator loss: 1.3919 *** generator loss: 0.8291
epoch 1/2 *** discriminator loss: 1.0425 *** generator loss: 0.9522
epoch 1/2 *** discriminator loss: 1.1164 *** generator loss: 1.0989
epoch 1/2 *** discriminator loss: 1.2998 *** generator loss: 1.8061
epoch 1/2 *** discriminator loss: 1.3268 *** generator loss: 1.0929
epoch 1/2 *** discriminator loss: 1.3855 *** generator loss: 1.2955
epoch 1/2 *** discriminator loss: 1.3174 *** generator loss: 0.5680
progress: 5%
epoch 1/2 *** discriminator loss: 1.3048 *** generator loss: 1.6636
epoch 1/2 *** discriminator loss: 1.2506 *** generator loss: 1.4653
epoch 1/2 *** discriminator loss: 1.3005 *** generator loss: 1.6013
epoch 1/2 *** discriminator loss: 1.3124 *** generator loss: 0.8109
epoch 1/2 *** discriminator loss: 1.2016 *** generator loss: 1.2517
epoch 1/2 *** discriminator loss: 1.6467 *** generator loss: 0.3564
epoch 1/2 *** discriminator loss: 1.6989 *** generator loss: 1.9236
epoch 1/2 *** discriminator loss: 1.1428 *** generator loss: 1.4835
epoch 1/2 *** discriminator loss: 1.2556 *** generator loss: 1.7431
epoch 1/2 *** discriminator loss: 1.1776 *** generator loss: 1.1481
progress: 6%
epoch 1/2 *** discriminator loss: 1.3628 *** generator loss: 0.6140
epoch 1/2 *** discriminator loss: 1.2152 *** generator loss: 0.8017
epoch 1/2 *** discriminator loss: 1.3919 *** generator loss: 1.3980
epoch 1/2 *** discriminator loss: 1.4803 *** generator loss: 1.6599
epoch 1/2 *** discriminator loss: 1.4925 *** generator loss: 0.7521
epoch 1/2 *** discriminator loss: 1.3567 *** generator loss: 0.6539
epoch 1/2 *** discriminator loss: 1.4777 *** generator loss: 0.4581
epoch 1/2 *** discriminator loss: 1.3408 *** generator loss: 0.6393
epoch 1/2 *** discriminator loss: 1.3038 *** generator loss: 0.6848
epoch 1/2 *** discriminator loss: 1.2048 *** generator loss: 1.1719
progress: 8%
epoch 1/2 *** discriminator loss: 1.1700 *** generator loss: 1.3246
epoch 1/2 *** discriminator loss: 1.2715 *** generator loss: 0.9029
epoch 1/2 *** discriminator loss: 1.2195 *** generator loss: 1.6867
epoch 1/2 *** discriminator loss: 1.5150 *** generator loss: 2.2470
epoch 1/2 *** discriminator loss: 1.1926 *** generator loss: 1.2033
epoch 1/2 *** discriminator loss: 1.3734 *** generator loss: 0.6758
epoch 1/2 *** discriminator loss: 1.3815 *** generator loss: 1.2253
epoch 1/2 *** discriminator loss: 1.2676 *** generator loss: 0.6016
epoch 1/2 *** discriminator loss: 1.4986 *** generator loss: 1.6763
epoch 1/2 *** discriminator loss: 1.1770 *** generator loss: 1.3790
progress: 10%
epoch 1/2 *** discriminator loss: 1.2019 *** generator loss: 1.4087
epoch 1/2 *** discriminator loss: 1.3304 *** generator loss: 1.2411
epoch 1/2 *** discriminator loss: 1.5943 *** generator loss: 0.4975
epoch 1/2 *** discriminator loss: 1.2663 *** generator loss: 0.9265
epoch 1/2 *** discriminator loss: 1.3126 *** generator loss: 1.2475
epoch 1/2 *** discriminator loss: 1.2750 *** generator loss: 0.9567
epoch 1/2 *** discriminator loss: 1.2259 *** generator loss: 0.9150
epoch 1/2 *** discriminator loss: 1.2096 *** generator loss: 0.9209
epoch 1/2 *** discriminator loss: 1.1317 *** generator loss: 1.0974
epoch 1/2 *** discriminator loss: 1.3422 *** generator loss: 1.3984
progress: 11%
epoch 1/2 *** discriminator loss: 1.3372 *** generator loss: 0.7783
epoch 1/2 *** discriminator loss: 1.2593 *** generator loss: 1.2201
epoch 1/2 *** discriminator loss: 1.2335 *** generator loss: 0.9393
epoch 1/2 *** discriminator loss: 1.1065 *** generator loss: 0.7433
epoch 1/2 *** discriminator loss: 1.0798 *** generator loss: 1.2341
epoch 1/2 *** discriminator loss: 1.1654 *** generator loss: 1.0516
epoch 1/2 *** discriminator loss: 1.4050 *** generator loss: 1.5048
epoch 1/2 *** discriminator loss: 1.4985 *** generator loss: 0.5748
epoch 1/2 *** discriminator loss: 1.2942 *** generator loss: 1.4229
epoch 1/2 *** discriminator loss: 1.1804 *** generator loss: 1.0125
progress: 13%
epoch 1/2 *** discriminator loss: 1.3075 *** generator loss: 1.1259
epoch 1/2 *** discriminator loss: 1.3515 *** generator loss: 0.6843
epoch 1/2 *** discriminator loss: 1.2408 *** generator loss: 1.0935
epoch 1/2 *** discriminator loss: 1.3482 *** generator loss: 1.0325
epoch 1/2 *** discriminator loss: 1.0058 *** generator loss: 0.9295
epoch 1/2 *** discriminator loss: 1.3243 *** generator loss: 0.7460
epoch 1/2 *** discriminator loss: 1.1865 *** generator loss: 0.9858
epoch 1/2 *** discriminator loss: 1.4144 *** generator loss: 1.2584
epoch 1/2 *** discriminator loss: 1.2358 *** generator loss: 1.0550
epoch 1/2 *** discriminator loss: 1.2917 *** generator loss: 0.8717
progress: 15%
epoch 1/2 *** discriminator loss: 1.3316 *** generator loss: 0.9089
epoch 1/2 *** discriminator loss: 1.2377 *** generator loss: 1.1089
epoch 1/2 *** discriminator loss: 1.3510 *** generator loss: 1.2125
epoch 1/2 *** discriminator loss: 1.2624 *** generator loss: 1.0123
epoch 1/2 *** discriminator loss: 1.2411 *** generator loss: 0.9326
epoch 1/2 *** discriminator loss: 1.3699 *** generator loss: 1.6424
epoch 1/2 *** discriminator loss: 1.4158 *** generator loss: 0.8287
epoch 1/2 *** discriminator loss: 1.1595 *** generator loss: 1.2941
epoch 1/2 *** discriminator loss: 1.3116 *** generator loss: 1.5743
epoch 1/2 *** discriminator loss: 1.2799 *** generator loss: 1.2270
progress: 16%
epoch 1/2 *** discriminator loss: 1.4484 *** generator loss: 0.9362
epoch 1/2 *** discriminator loss: 1.3043 *** generator loss: 1.0184
epoch 1/2 *** discriminator loss: 1.2877 *** generator loss: 1.0991
epoch 1/2 *** discriminator loss: 1.2618 *** generator loss: 0.9614
epoch 1/2 *** discriminator loss: 1.1668 *** generator loss: 0.9985
epoch 1/2 *** discriminator loss: 1.2092 *** generator loss: 1.2846
epoch 1/2 *** discriminator loss: 1.2637 *** generator loss: 1.2693
epoch 1/2 *** discriminator loss: 1.1245 *** generator loss: 1.2151
epoch 1/2 *** discriminator loss: 1.4118 *** generator loss: 0.9132
epoch 1/2 *** discriminator loss: 1.4148 *** generator loss: 0.9482
progress: 18%
epoch 1/2 *** discriminator loss: 1.1150 *** generator loss: 1.1060
epoch 1/2 *** discriminator loss: 1.3155 *** generator loss: 1.4157
epoch 1/2 *** discriminator loss: 1.2569 *** generator loss: 1.2441
epoch 1/2 *** discriminator loss: 1.2054 *** generator loss: 1.3470
epoch 1/2 *** discriminator loss: 1.2561 *** generator loss: 0.7996
epoch 1/2 *** discriminator loss: 1.2995 *** generator loss: 1.0644
epoch 1/2 *** discriminator loss: 1.5568 *** generator loss: 1.6529
epoch 1/2 *** discriminator loss: 1.3540 *** generator loss: 1.0335
epoch 1/2 *** discriminator loss: 1.2164 *** generator loss: 1.4190
epoch 1/2 *** discriminator loss: 1.3529 *** generator loss: 1.3860
progress: 19%
epoch 1/2 *** discriminator loss: 1.2768 *** generator loss: 1.0609
epoch 1/2 *** discriminator loss: 1.2798 *** generator loss: 0.6264
epoch 1/2 *** discriminator loss: 1.2682 *** generator loss: 1.0188
epoch 1/2 *** discriminator loss: 1.3930 *** generator loss: 0.6823
epoch 1/2 *** discriminator loss: 1.2199 *** generator loss: 1.2461
epoch 1/2 *** discriminator loss: 1.0341 *** generator loss: 0.9835
epoch 1/2 *** discriminator loss: 1.5044 *** generator loss: 1.3875
epoch 1/2 *** discriminator loss: 1.2057 *** generator loss: 0.9067
epoch 1/2 *** discriminator loss: 1.5025 *** generator loss: 0.5225
epoch 1/2 *** discriminator loss: 1.1533 *** generator loss: 1.1066
progress: 21%
epoch 1/2 *** discriminator loss: 1.4942 *** generator loss: 0.9109
epoch 1/2 *** discriminator loss: 1.3444 *** generator loss: 1.0061
epoch 1/2 *** discriminator loss: 1.1575 *** generator loss: 0.9725
epoch 1/2 *** discriminator loss: 1.1177 *** generator loss: 1.5442
epoch 1/2 *** discriminator loss: 1.3256 *** generator loss: 0.7256
epoch 1/2 *** discriminator loss: 1.3621 *** generator loss: 1.7371
epoch 1/2 *** discriminator loss: 1.2322 *** generator loss: 0.9759
epoch 1/2 *** discriminator loss: 1.1785 *** generator loss: 0.9884
epoch 1/2 *** discriminator loss: 1.3008 *** generator loss: 0.8278
epoch 1/2 *** discriminator loss: 1.3159 *** generator loss: 0.5787
progress: 23%
epoch 1/2 *** discriminator loss: 1.3154 *** generator loss: 0.6671
epoch 1/2 *** discriminator loss: 1.4497 *** generator loss: 0.5999
epoch 1/2 *** discriminator loss: 1.2719 *** generator loss: 0.7672
epoch 1/2 *** discriminator loss: 1.2051 *** generator loss: 1.1166
epoch 1/2 *** discriminator loss: 1.3116 *** generator loss: 0.8612
epoch 1/2 *** discriminator loss: 1.2164 *** generator loss: 1.1250
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epoch 1/2 *** discriminator loss: 1.3362 *** generator loss: 1.1881
epoch 1/2 *** discriminator loss: 1.2401 *** generator loss: 1.3163
progress: 24%
epoch 1/2 *** discriminator loss: 1.4510 *** generator loss: 0.7087
epoch 1/2 *** discriminator loss: 1.0861 *** generator loss: 1.0593
epoch 1/2 *** discriminator loss: 1.4430 *** generator loss: 0.7576
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epoch 1/2 *** discriminator loss: 1.5084 *** generator loss: 0.7010
epoch 1/2 *** discriminator loss: 1.2979 *** generator loss: 1.3736
epoch 1/2 *** discriminator loss: 1.3175 *** generator loss: 1.7747
progress: 26%
epoch 1/2 *** discriminator loss: 1.0318 *** generator loss: 1.1119
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epoch 1/2 *** discriminator loss: 1.3320 *** generator loss: 0.8407
epoch 1/2 *** discriminator loss: 1.1218 *** generator loss: 1.0497
epoch 1/2 *** discriminator loss: 0.9670 *** generator loss: 1.1542
epoch 1/2 *** discriminator loss: 1.2084 *** generator loss: 1.2913
epoch 1/2 *** discriminator loss: 1.3490 *** generator loss: 0.8597
epoch 1/2 *** discriminator loss: 1.2311 *** generator loss: 0.9192
epoch 1/2 *** discriminator loss: 1.2974 *** generator loss: 0.8074
epoch 1/2 *** discriminator loss: 1.0969 *** generator loss: 1.1148
progress: 27%
epoch 1/2 *** discriminator loss: 1.2333 *** generator loss: 1.2736
epoch 1/2 *** discriminator loss: 1.5182 *** generator loss: 0.7849
epoch 1/2 *** discriminator loss: 1.2272 *** generator loss: 1.0671
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epoch 1/2 *** discriminator loss: 1.0868 *** generator loss: 1.1094
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epoch 1/2 *** discriminator loss: 1.1560 *** generator loss: 1.3772
epoch 1/2 *** discriminator loss: 1.1747 *** generator loss: 1.2904
epoch 1/2 *** discriminator loss: 1.1477 *** generator loss: 1.1141
epoch 1/2 *** discriminator loss: 1.4902 *** generator loss: 0.7940
progress: 29%
epoch 1/2 *** discriminator loss: 1.0850 *** generator loss: 0.9536
epoch 1/2 *** discriminator loss: 1.1068 *** generator loss: 1.1584
epoch 1/2 *** discriminator loss: 1.3583 *** generator loss: 1.1682
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epoch 1/2 *** discriminator loss: 1.3033 *** generator loss: 1.3783
epoch 1/2 *** discriminator loss: 1.4500 *** generator loss: 0.5878
epoch 2/2 *** discriminator loss: 1.2228 *** generator loss: 1.2960
epoch 2/2 *** discriminator loss: 1.1756 *** generator loss: 1.4544
epoch 2/2 *** discriminator loss: 1.1508 *** generator loss: 1.4836
progress: 31%
epoch 2/2 *** discriminator loss: 1.6549 *** generator loss: 0.7715
epoch 2/2 *** discriminator loss: 1.1895 *** generator loss: 1.3255
epoch 2/2 *** discriminator loss: 1.4851 *** generator loss: 1.5939
epoch 2/2 *** discriminator loss: 1.1603 *** generator loss: 0.9345
epoch 2/2 *** discriminator loss: 1.2080 *** generator loss: 1.0484
epoch 2/2 *** discriminator loss: 1.1744 *** generator loss: 1.1778
epoch 2/2 *** discriminator loss: 1.3253 *** generator loss: 0.9997
epoch 2/2 *** discriminator loss: 1.1713 *** generator loss: 0.9901
epoch 2/2 *** discriminator loss: 1.2161 *** generator loss: 0.6231
epoch 2/2 *** discriminator loss: 1.3632 *** generator loss: 1.0398
progress: 32%
epoch 2/2 *** discriminator loss: 1.1290 *** generator loss: 1.2678
epoch 2/2 *** discriminator loss: 1.2245 *** generator loss: 0.9408
epoch 2/2 *** discriminator loss: 1.1124 *** generator loss: 1.1632
epoch 2/2 *** discriminator loss: 1.1611 *** generator loss: 1.0998
epoch 2/2 *** discriminator loss: 1.2662 *** generator loss: 1.0740
epoch 2/2 *** discriminator loss: 1.0414 *** generator loss: 0.8763
epoch 2/2 *** discriminator loss: 1.0729 *** generator loss: 1.0797
epoch 2/2 *** discriminator loss: 1.1503 *** generator loss: 0.9597
epoch 2/2 *** discriminator loss: 1.3111 *** generator loss: 1.9439
epoch 2/2 *** discriminator loss: 1.1361 *** generator loss: 1.1310
progress: 34%
epoch 2/2 *** discriminator loss: 1.1665 *** generator loss: 1.2315
epoch 2/2 *** discriminator loss: 1.3273 *** generator loss: 1.4584
epoch 2/2 *** discriminator loss: 1.2025 *** generator loss: 1.1356
epoch 2/2 *** discriminator loss: 1.0762 *** generator loss: 0.9409
epoch 2/2 *** discriminator loss: 1.2473 *** generator loss: 1.7117
epoch 2/2 *** discriminator loss: 1.1702 *** generator loss: 1.1807
epoch 2/2 *** discriminator loss: 1.2634 *** generator loss: 1.1850
epoch 2/2 *** discriminator loss: 1.2992 *** generator loss: 0.9733
epoch 2/2 *** discriminator loss: 1.2933 *** generator loss: 1.3108
epoch 2/2 *** discriminator loss: 1.2387 *** generator loss: 1.3230
progress: 35%
epoch 2/2 *** discriminator loss: 1.0554 *** generator loss: 1.0401
epoch 2/2 *** discriminator loss: 1.2050 *** generator loss: 1.5836
epoch 2/2 *** discriminator loss: 1.1046 *** generator loss: 1.1991
epoch 2/2 *** discriminator loss: 1.1107 *** generator loss: 1.4259
epoch 2/2 *** discriminator loss: 1.1902 *** generator loss: 0.7583
epoch 2/2 *** discriminator loss: 1.2015 *** generator loss: 0.9225
epoch 2/2 *** discriminator loss: 1.1707 *** generator loss: 1.1407
epoch 2/2 *** discriminator loss: 1.2298 *** generator loss: 1.0113
epoch 2/2 *** discriminator loss: 1.5057 *** generator loss: 1.5025
epoch 2/2 *** discriminator loss: 1.1736 *** generator loss: 1.1802
progress: 37%
epoch 2/2 *** discriminator loss: 1.3231 *** generator loss: 1.4374
epoch 2/2 *** discriminator loss: 1.3809 *** generator loss: 1.8263
epoch 2/2 *** discriminator loss: 1.0769 *** generator loss: 0.9495
epoch 2/2 *** discriminator loss: 1.4393 *** generator loss: 0.9071
epoch 2/2 *** discriminator loss: 1.2432 *** generator loss: 0.9496
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epoch 2/2 *** discriminator loss: 1.2629 *** generator loss: 1.2234
epoch 2/2 *** discriminator loss: 1.2746 *** generator loss: 1.0728
progress: 39%
epoch 2/2 *** discriminator loss: 1.0671 *** generator loss: 0.8088
epoch 2/2 *** discriminator loss: 1.1392 *** generator loss: 0.8131
epoch 2/2 *** discriminator loss: 1.2844 *** generator loss: 0.8631
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epoch 2/2 *** discriminator loss: 1.3429 *** generator loss: 0.7506
epoch 2/2 *** discriminator loss: 1.3716 *** generator loss: 1.3345
progress: 40%
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epoch 2/2 *** discriminator loss: 1.0813 *** generator loss: 1.2702
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epoch 2/2 *** discriminator loss: 1.2494 *** generator loss: 0.8825
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epoch 2/2 *** discriminator loss: 1.1531 *** generator loss: 1.1369
epoch 2/2 *** discriminator loss: 1.3602 *** generator loss: 0.7078
progress: 42%
epoch 2/2 *** discriminator loss: 1.1690 *** generator loss: 0.9442
epoch 2/2 *** discriminator loss: 1.1033 *** generator loss: 1.0263
epoch 2/2 *** discriminator loss: 1.1307 *** generator loss: 1.1194
epoch 2/2 *** discriminator loss: 1.1920 *** generator loss: 1.3018
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epoch 2/2 *** discriminator loss: 1.3800 *** generator loss: 0.5559
epoch 2/2 *** discriminator loss: 1.2318 *** generator loss: 0.7073
progress: 44%
epoch 2/2 *** discriminator loss: 1.3336 *** generator loss: 1.6090
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epoch 2/2 *** discriminator loss: 1.1155 *** generator loss: 1.1276
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progress: 45%
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progress: 47%
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epoch 2/2 *** discriminator loss: 1.3388 *** generator loss: 0.9723
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epoch 2/2 *** discriminator loss: 1.0518 *** generator loss: 1.2019
epoch 2/2 *** discriminator loss: 1.3672 *** generator loss: 1.6316
progress: 48%
epoch 2/2 *** discriminator loss: 1.4310 *** generator loss: 1.1915
epoch 2/2 *** discriminator loss: 1.1521 *** generator loss: 1.1159
epoch 2/2 *** discriminator loss: 1.2383 *** generator loss: 1.3361
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epoch 2/2 *** discriminator loss: 1.3588 *** generator loss: 0.6526
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epoch 2/2 *** discriminator loss: 0.9803 *** generator loss: 1.3561
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epoch 2/2 *** discriminator loss: 1.1218 *** generator loss: 1.3180
epoch 2/2 *** discriminator loss: 1.1235 *** generator loss: 1.1069
progress: 50%
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epoch 2/2 *** discriminator loss: 1.2408 *** generator loss: 1.0392
epoch 2/2 *** discriminator loss: 1.1848 *** generator loss: 1.4321
progress: 52%
epoch 2/2 *** discriminator loss: 1.1269 *** generator loss: 0.8818
epoch 2/2 *** discriminator loss: 1.1973 *** generator loss: 0.8025
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epoch 2/2 *** discriminator loss: 1.1181 *** generator loss: 1.4117
epoch 2/2 *** discriminator loss: 1.1503 *** generator loss: 0.9635
progress: 53%
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epoch 2/2 *** discriminator loss: 1.2335 *** generator loss: 0.9941
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progress: 55%
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epoch 2/2 *** discriminator loss: 1.2668 *** generator loss: 1.5934
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progress: 56%
epoch 2/2 *** discriminator loss: 0.9429 *** generator loss: 1.0322
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epoch 2/2 *** discriminator loss: 1.1760 *** generator loss: 1.1866
epoch 2/2 *** discriminator loss: 1.3117 *** generator loss: 1.1547
progress: 58%
epoch 2/2 *** discriminator loss: 1.4729 *** generator loss: 1.0585
epoch 2/2 *** discriminator loss: 1.2071 *** generator loss: 1.1070
epoch 2/2 *** discriminator loss: 1.2448 *** generator loss: 1.0705
epoch 2/2 *** discriminator loss: 1.0048 *** generator loss: 1.3638
epoch 2/2 *** discriminator loss: 1.3690 *** generator loss: 0.9044
epoch 2/2 *** discriminator loss: 1.2356 *** generator loss: 1.4004
epoch 2/2 *** discriminator loss: 1.2353 *** generator loss: 1.1781
epoch 2/2 *** discriminator loss: 1.3581 *** generator loss: 1.5629
epoch 2/2 *** discriminator loss: 0.9819 *** generator loss: 1.6531
epoch 2/2 *** discriminator loss: 1.1344 *** generator loss: 1.2976
progress: 60%
epoch 2/2 *** discriminator loss: 1.1074 *** generator loss: 0.9355
epoch 2/2 *** discriminator loss: 0.9897 *** generator loss: 1.5342
epoch 2/2 *** discriminator loss: 1.3901 *** generator loss: 1.1936
epoch 2/2 *** discriminator loss: 1.2578 *** generator loss: 2.0155
epoch 2/2 *** discriminator loss: 1.2211 *** generator loss: 1.7163
(20, 40)
COMPLETE

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 64
z_dim = 128
learning_rate = 0.001
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
epoch 1/1 *** discriminator loss: 1.7166 *** generator loss: 6.0176
epoch 1/1 *** discriminator loss: 1.8230 *** generator loss: 4.8203
epoch 1/1 *** discriminator loss: 1.4507 *** generator loss: 6.3856
epoch 1/1 *** discriminator loss: 1.3640 *** generator loss: 5.7880
epoch 1/1 *** discriminator loss: 0.9961 *** generator loss: 3.7263
epoch 1/1 *** discriminator loss: 2.1505 *** generator loss: 5.3661
epoch 1/1 *** discriminator loss: 0.6555 *** generator loss: 2.4644
epoch 1/1 *** discriminator loss: 0.7365 *** generator loss: 1.5685
epoch 1/1 *** discriminator loss: 0.6744 *** generator loss: 1.9143
epoch 1/1 *** discriminator loss: 0.8950 *** generator loss: 2.7126
progress: 3%
epoch 1/1 *** discriminator loss: 0.7456 *** generator loss: 1.9363
epoch 1/1 *** discriminator loss: 0.7664 *** generator loss: 1.3424
epoch 1/1 *** discriminator loss: 1.2982 *** generator loss: 4.5497
epoch 1/1 *** discriminator loss: 0.8125 *** generator loss: 2.0980
epoch 1/1 *** discriminator loss: 0.9973 *** generator loss: 1.1571
epoch 1/1 *** discriminator loss: 0.6579 *** generator loss: 2.2768
epoch 1/1 *** discriminator loss: 0.8091 *** generator loss: 1.3729
epoch 1/1 *** discriminator loss: 0.8524 *** generator loss: 1.4766
epoch 1/1 *** discriminator loss: 0.9700 *** generator loss: 1.6869
epoch 1/1 *** discriminator loss: 1.3121 *** generator loss: 0.5154
progress: 6%
epoch 1/1 *** discriminator loss: 1.2338 *** generator loss: 2.8402
epoch 1/1 *** discriminator loss: 1.5215 *** generator loss: 0.5133
epoch 1/1 *** discriminator loss: 0.8607 *** generator loss: 1.3917
epoch 1/1 *** discriminator loss: 0.7231 *** generator loss: 1.3540
epoch 1/1 *** discriminator loss: 1.0996 *** generator loss: 1.1848
epoch 1/1 *** discriminator loss: 0.8861 *** generator loss: 1.2745
epoch 1/1 *** discriminator loss: 1.0627 *** generator loss: 2.2791
epoch 1/1 *** discriminator loss: 1.2777 *** generator loss: 2.4940
epoch 1/1 *** discriminator loss: 1.6361 *** generator loss: 3.1623
epoch 1/1 *** discriminator loss: 1.2897 *** generator loss: 2.3761
progress: 10%
epoch 1/1 *** discriminator loss: 0.9695 *** generator loss: 1.2537
epoch 1/1 *** discriminator loss: 1.5255 *** generator loss: 0.5416
epoch 1/1 *** discriminator loss: 1.1632 *** generator loss: 2.1118
epoch 1/1 *** discriminator loss: 1.1031 *** generator loss: 0.8760
epoch 1/1 *** discriminator loss: 1.0745 *** generator loss: 2.0031
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progress: 13%
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progress: 16%
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progress: 39%
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progress: 42%
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progress: 55%
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progress: 65%
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progress: 97%
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progress: 100%
epoch 1/1 *** discriminator loss: 1.2622 *** generator loss: 0.8783
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epoch 1/1 *** discriminator loss: 1.3048 *** generator loss: 0.7980
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(45, 45)
COMPLETE
In [ ]:
 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.